Color interpolation method of an image acquired by a digital sensor by directional filtering

ABSTRACT

Subdivision per basic color channels of grey level data generated by a color sensor is no longer required according to a novel color interpolation method of an image acquired by a digital color sensor generating grey levels for each image pixel as a function of the filter applied to the sensor by interpolating the values of missing colors of each image pixel for generating triplets or pairs of values of primary colors or complementary base hues for each image pixel. The method may include calculating spatial variation gradients of primary colors or complementary base hues for each image pixel and storing the information of directional variation of primary color or complementary base hue in look-up tables pertaining to each pixel. The method may further include interpolating color values of each image pixel considering the directional variation information of the respective values of primary colors or complementary hues stored in the respective look-up tables of the pixel for generating the multiple distinct values for each image pixel.

FIELD OF THE INVENTION

This invention relates to techniques for processing images acquired by acolor digital sensor, and, more particularly, to a method of colorinterpolation of each pixel of the image acquired from intensity valuesgenerated by single photosensitive elements of the sensor, depending onthe filter applied according to a certain spatial pattern (for instanceaccording the so-called Bayer Pattern) such to make each single elementsensitive to one of the primary colors or base hue. Therefore, theoutput of a digital color sensor generates a grey level value for eachimage pixel depending on the filter applied to the particular imagepixel.

BACKGROUND OF THE INVENTION

In order to reconstruct a color image it is usually necessary to carryout an operation known as color interpolation (or demosaicing) togenerate triplets of base color values (RGB) or more values, one foreach base hue, through appropriate interpolation algorithms forgenerating values of missing colors for each image pixel. Numeroustechniques for processing data provided by a digital color sensor havebeen proposed. It is worth mentioning the following documents: M. R.Gupta, T. Chen, “Vector Color Filter Array Demosaicing” SPIE ElectronicImaging 2001; R. Ramanath, W. E. Snyder, G. L. Bilbro, W. A. Sander,“Demosaicing Methods for Bayer Color Arrays”, Journal of ElectronicImaging, vol. 11, n. 3, pages 306-315, July 2002; R. Kimmel,“Demosaicing: Image Reconstruction from Color CCD Samples”; R. Kakarala,Z. Baharav, “Adaptive Demosaicing with The Principal Vector Method”,IEEE Transactions on Consumer Electronics, vol. 48, n. 4, pages 932-937,November 2002; B. E. Bayer, “Color Imaging Array”, U.S. Pat. No.3,971,065, July 1976; B. K. Gunturk, Y. Altunbasak, R, Mersereau, “ColorPlane Interpolation Using Alternating Projections”, IEEE Transactions onImage Processing, vol. 11, no. 9, pages 997-1013, September 2002; S.Smith, “Colour Image Restoration with Anti-Alias”, EP 1,098,535, May2001. Many known techniques preliminarily subdivide the image datastream generated by the digital color sensor into two or more channels,such as three channels for the case of a filtering based upon the RGBtriplet of primary colors (red, green, blue). When the red component ofa pixel is to be estimated, but only its green level has been acquired,it is necessary to estimate the red pixels adjacent to the consideredpixel, and so on, when the value of another missing color is to beestimated. Clearly, subdividing in different channels grey level datagenerated by the digital color sensor and the successive mergingoperation of the values calculated for primary colors or base hues withthe known value of the primary color of base hue for the consideredpixel implies an evident computational burden or, in case of hardwareimplementation, an increased circuit complexity.

The development of new consumer applications of digital photo-camerasand similar devices, for instance in cellular phones, in laptop(notebook) or hand-held computers, and other devices for mobilecommunications, encourages the need to devise more effective and at thesame time low cost techniques for processing images acquired by adigital color sensor. A particular important factor is the low cost,because these techniques may desirably be used in devices economicallyaccessible to individual consumers, and there is considerablecompetition in this field among manufacturers of these devices and theircomponents.

SUMMARY OF THE INVENTION

It is an object of the invention to provide a new color interpolationalgorithm that may not require any subdivision per channels of greylevel data generated by a digital color sensor. According to thealgorithm of this invention, the effectiveness in terms of definitionand color rendition of the processed picture may be higher than thatobtained using known methods based on subdivision per channels of dataand on merging of data in terms of triplets or pairs of values ofprimary colors or complementary hues, respectively.

Basically, the color interpolation method of an image may be acquired bya digital color sensor generating grey levels for each image pixel as afunction of the filter applied to the sensor, by interpolating thevalues of missing colors of each image pixel for generating triplets orpairs of values of primary colors (RGB) or complementary hues for eachimage pixel. The method may comprise calculating spatial variationgradients of primary colors or complementary hues for each image pixeland storing the information of directional variation of primary color orcomplementary hue in look-up tables pertaining to each pixel.

The method may further comprise interpolating color values of each imagepixel considering the directional variation information of therespective values of primary colors or complementary hues stored in therespective look-up tables of the pixel for generating the multipledistinct values for each image pixel.

According to a preferred embodiment, spatial variation gradients may becalculated using differential filters, preferably Sobel operators overimage pixels detected without subdividing them depending on the color.Preferably, the interpolation of the respective values of primary colorsor base hue may be carried out using an elliptical Gaussian filter theorientation of which coincides with the calculated direction of therespective spatial gradient for the pixel being processed.

In order to compensate for the emphasis introduced by calculating thespatial gradients of the low spatial frequency components, due to thefact that values of the same primary color or complementary hue of thepixel adjacent to the processed pixel are used, the method mayoptionally include an additional enhancement operation of the highspatial frequency components. The invention may further provide a simpleand effective method for calculating direction and amplitude values of aspatial gradient in correspondence of an image pixel. The methods ofthis invention may be implemented by a software computer program.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the correlation among different channels in a color picturein accordance with the invention.

FIG. 2 is a flow chart illustrating data flow in accordance with theinvention.

FIG. 3 shows quantized directions for spatial gradients of image pixelsin accordance with the invention.

FIGS. 4 a and 4 b show the same picture filtered with a weighted-modedirectional filter in accordance with the invention and without such afilter.

FIGS. 5 a and 5 b show two examples of frequency responses ofdirectional filters at π/2 and 0 in accordance with the invention.

FIG. 6 shows a Bayer pattern with a central green pixel in accordancewith the invention.

FIGS. 7 through 10 compare pictures obtained with the known method IGPand with the method of this invention using a directional filteringinterpolation (DF).

FIG. 11 compares different PSNR values of different images taken from astandard image database for the method of this invention and the knownmethod IGP.

FIG. 12 shows a sample device that carries out a color interpolationalgorithm in accordance with the invention.

FIG. 13 is a flow chart of an image enhancement process that uses themethod of this invention, indicated by the label DF.

FIG. 14 shows an architecture implementing the method of this invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

“Bayer pattern” filtering is based on the use of primary color filters:red (R), green (G) and blue (B). It is widely used in digital colorsensors and for this reason the method of this invention is nowdescribed in detail referring to this application, though the sameconsiderations apply mutatis mutandis for systems for acquiring colorimages based on filters of two or more complementary hues appliedaccording to spatial patterns different from the Bayer pattern.

According to the invention, it is supposed that the red and bluechannels, especially at low frequencies, be highly correlated with thegreen channel in a small interval, as shown in the example of FIG. 1.If, for example, the central pixel is red, it is possible to assume thatthe low-pass component of the green channel isG _(LPF)(i,j)=R _(LPF)(i,j)+ΔRG  (1)wherein ΔRG is an appropriate value that depends on the pixels thatsurround the considered pixel, andG _(LPF)(i,j)=f ₁(i,j)  (2)R _(LPF)(i,j)=f ₂(i,j)  (3)wherein f₁ and f₂ are interpolation functions of the central pixeldetermined from the surrounding pixel.

This implies that the interpolation, for instance, of the missing greenpixels may advantageously exploit the information coming from the redand blue channels. In practice, according to the invention, the missingpixels are interpolated without splitting the image into the componentchannels. This model uses differences between different channels (thatmay be seen as chromatic information), is susceptible of generatingoptimal results because the human eye is more sensitive to low frequencychromatic variations than to luminance variations.

In order to estimate the direction of an edge depicted in the picture itis necessary to calculate in the CFA (Color Filter Array) image thevariation along the horizontal and vertical directions. A preferred wayof calculating these variations comprises using the well known Sobelfilters along an horizontal x axis and a vertical y axis:

$\begin{matrix}{{{Sobel}_{y} = \begin{bmatrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{bmatrix}};{{Sobel}_{x} = \begin{bmatrix}1 & 0 & {- 1} \\2 & 0 & {- 2} \\1 & 0 & {- 1}\end{bmatrix}}} & (4)\end{matrix}$

So far, the Sobel filters have been used on the same channel (R or G orB), or on a same luminance channel. According to the invention, theSobel filters are used directly on the combination of the three channelsRGB. The demonstration that the Sobel filters can be usefully applied toa Bayer pattern is not simple and is given hereinafter.

Let us consider a 3×3 Bayer pattern:

$\begin{matrix}{P = \begin{bmatrix}G_{1} & J_{2} & G_{3} \\H_{4} & G_{5} & H_{6} \\G_{7} & J_{8} & G_{9}\end{bmatrix}} & (5)\end{matrix}$wherein G_(i) for i=1, 3, 5, 7, 9 are the intensities of the greenpixels and H_(i) and J_(i) are respectively the intensities of the redand blue pixels. Considering Eq. (1), it is possible to approximate theintensity of the missing green pixels.

It is thus obtained that the channel G (corresponding to the consideredcentral pixel in the example) is described by the following matrix P′:

$\begin{matrix}{P^{\prime} = \begin{bmatrix}G_{1} & {J_{2} + \Delta_{1}} & G_{3} \\{H_{4} + \Delta_{2}} & G_{5} & {H_{6} + \Delta_{3}} \\G_{7} & {J_{8} + \Delta_{4}} & G_{9}\end{bmatrix}} & (6)\end{matrix}$The convolution between the matrix P′ and the matrix that describes theSobel filter along the vertical direction (Sobel_(y)) is:

$\begin{matrix}\begin{matrix}{{P^{\prime} \cdot {Sobel}_{y}} = {\begin{bmatrix}G_{1} & {J_{2} + \Delta_{1}} & G_{3} \\{H_{4} + \Delta_{2}} & G_{5} & {H_{6} + \Delta_{3}} \\G_{7} & {J_{8} + \Delta_{4}} & G_{9}\end{bmatrix} \otimes \begin{bmatrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{bmatrix}}} \\{= {G_{1} + {2( {J_{2} + \Delta_{1}} )} + G_{3} - G_{7} - {2( {J_{8} + \Delta_{4}} )} - {G_{9}.}}}\end{matrix} & (7)\end{matrix}$

The unknown parameters are only Δ₁ and Δ₄. These two parameters areestimated in a small image portion, thus it may be reasonably presumedthat they are almost equal to each other and that their difference isnegligible. As a consequence, Eq. (7) becomesP′·Sobel _(y) =G ₁+2J ₂ +G ₃ −G ₇−2J ₈ −G ₉  (8)It may be immediately noticed that, by applying the Sobel filterdirectly on the Bayer pattern of Eq. (5), the same result given by Eq.(8) is obtained. Therefore, using this filter Sobel_(y) on a Bayerpattern is equivalent to calculating the corresponding spatial variationvalue along the y direction. By using the filter Sobel_(x) along thehorizontal direction, another value P′·Sobel_(x) is obtained thatprovides a corresponding spatial variation value along the x direction.For each pixel of the Bayer pattern the values P′·Sobel_(x) andP′·Sobel_(x) are calculated. According to the method of the invention,these values are collected in look-up tables for estimating thedirection of the reconstruction filter.

The orientation of the spatial gradient in correspondence to a certainpixel is given by the following formula:

$\begin{matrix}{{{or}( {x,y} )} = \{ \begin{matrix}{\tan^{- 1}( \frac{P^{\prime} \cdot {{Sobel}_{y}( {x,y} )}}{P^{\prime} \cdot {{Sobel}_{x}( {x,y} )}} )} & {{{if}\mspace{14mu}{P^{\prime} \cdot ( {{Sobel}_{x}( {x,y} )} )}} \neq 0} \\\frac{\pi}{2} & {otherwise}\end{matrix} } & (9)\end{matrix}$wherein P′·Sobel_(y) and P′·Sobel_(x) are the filtered values with thehorizontal and vertical Sobel filters centered in a given pixel. Theorientation or (x,y) is preferably quantized in k pre-defineddirections, for instance those depicted in FIG. 3:

$\begin{matrix}{{{direction}_{i} = \frac{i \cdot \pi}{k}},{{{wherein}\mspace{14mu} i} = \lbrack {0,{k - 1}} \rbrack},{k\;\varepsilon\;\aleph}} & (10)\end{matrix}$

If the calculated orientation value or(x,y) belongs to a fixed intervali, the orientation will be quantized by the value direction_(i):or(x,y)={direction_(i)|direction_(i)≦or(x,y)<direction_(i+1)}  (11)The square of the absolute value of the spatial gradient mag(x,y) iscalculated according to the following formula:mag(x,y)=(P′·Sobel _(x))²+(P′·Sobel _(y))²  (12)It is preferable to consider the square of the absolute value foravoiding square roots, the calculation of which would slow down themethod of the invention. For each calculated orientation of the spatialgradients, a new operator, purposely designed for the preferredembodiment of the method of the invention, that hereinafter will beindicated as the “weighted-mode” (WM) operator, is used. This operatorsubstantially provides an estimation of the predominant amplitude of thespatial gradient around the central pixel by performing the operationsof: storing the amplitude values for each pixel around a central pixelaccording to the following formula

$\begin{matrix}{{{Acc}( {x,y,i} )} = {\sum\limits_{u = {- 1}}^{1}\;{\sum\limits_{v = {- 1}}^{1}\;{{{mag}( {{x + u},{y + v}} )} \cdot {t( {{x + u},{y + v},i} )}}}}} & (13)\end{matrix}$wherein u=[−1, +1], v=[−1, +1], iε[0, k−1], kε

and Acc is an array of k possible orientations;

$\begin{matrix}{{t( {x,y,i} )} = \{ \begin{matrix}0 & {{{if}\mspace{14mu}{{or}( {x,y} )}} \neq {direction}_{i}} \\1 & {otherwise}\end{matrix} } & ( {13\mspace{14mu}{bis}} )\end{matrix}$calculating the predominant amplitude WM with the following formula:

$\begin{matrix}{{{WM}( {x,y} )} = {\max\limits_{i = {{0...}k}}( {{Acc}( {x,y,i} )} )}} & (14)\end{matrix}$

The operator WM is a sort of morphological operator that enhance theedges. This operator is used for preventing the effects due to thepresence of noise in estimating the spatial gradient. According to anembodiment of the invention, the interpolation filtering is carried outby means of an elliptical Gaussian filter described by the followingformula:

$\begin{matrix}{{{f( {x,y,\alpha} )} = {h \cdot {\mathbb{e}}^{{- \frac{{\overset{\sim}{x}}^{2}}{2\sigma_{x}^{2}}} - \frac{{\overset{\sim}{y}}^{2}}{2\sigma_{y}^{2}}}}},} & (15)\end{matrix}$wherein{tilde over (x)}=x cos(α)−y sin(α),{tilde over (y)}=x sin(α)+y sin(α),  (16)and σ_(x), σ_(y) are the standard deviations along the directions x andy, respectively, h is a normalization constant and α is the orientationangle, as shown in FIG. 5. According to the invention, the orientationof the filter to be applied in a pixel coincides with the orientation ofthe spatial filter calculated for that pixel. The filter provides thelow-pass red R_(LPF), green G_(LPF) and blue B_(LPF) components,preserving the image from zig-zag effects and false colors (artifacts).

A low-pass component is calculated using a spectral formulation, asdefined in the following formula:Comp(l,m)=Mask(l,m)·F(l,m)·I(l,m)  (17)wherein Mask (l,m) identifies the pixels of the spectral components(l,m) (red, green or blue), F is one of the above mentioned Gaussianfilters and I is the input picture.

In order to further enhance the quality of the generated picture, it ispossible to carry out the so-called “peaking” operation, thatsubstantially comprises introducing in the filtered image high frequencycomponents that were lost during the low-pass filtering with thedirectional Gaussian filter. This operation will be described referringto the case in which the-central pixel of the selection frequency of theBayer pattern is a green pixel G. Being G_(LPF) _(—) _(DF) the low-passcomponent of this pixel obtained with the directional filter, the losthigh frequency component Δ_(Peak) is given by the following equationΔ_(Peak) =G−G _(LPF) _(—) _(DF)  (18)

In practice, the high frequency component is obtained as the differencebetween the known effective value of the central pixel G and thecorresponding low-pass filtered value G_(LPF) _(—) _(DF). The centralpixel is kept unchanged in the interpolated image, while thereconstructed low-pass blue and red values are increased by the quantityΔ_(Peak) for enhancing the high frequency components. Substantially, theintensity H of the red and blue central pixels is obtained with thefollowing formula:H=H _(LPF) _(—) _(DF)+Δ_(Peak)  (19)Various experiments have been carried out for comparing original imageswith images filtered with the method of this invention using adirectional filtering (DF), and with images interpolated with the knownmethod IGP [7]. The method of this invention has been implemented withthe just described “peaking” operation. The results are shown in theFIGS. from 7 to 10.

FIG. 11 compares the PSNR values obtained for the method of thisinvention and the known method IGP for different images extracted from astandard database of images (http://r0k.us/graphics/kodak). As it ispossible to notice, the method of the invention based on the directionalfiltering DF ensures higher signal/noise ratios than the known methodIGP.

The color interpolation method of the invention may be incorporated inany process for treating digital Bayer images. Tests have shown that themethod of this invention relevantly limits the generation of falsecolors and/or zig-zag effects, compared to the known color interpolationmethods. In order to further improve the quality of images obtained withany process for treating Bayer images including the method of theinvention, it is possible to carry out any anti-aliasing algorithm.

FIG. 12 shows a sample device, such as a digital photo-camera, thatcarries out a color interpolation algorithm. FIG. 13 is a flow chart ofa process for treating images comprising the method of the invention,schematically indicated by the label DF.

FIG. 14 shows an architecture implementing the preferred embodiment ofthe method of this invention, wherein each single block carries out oneof the above operations. The meaning of each functional block will beevident for any skilled person, thus this architecture will not bedescribed.

1. A method of color interpolation of an image acquired by a digitalcolor sensor generating grey level values for each image pixel as afunction of a filter applied to the sensor by interpolating values ofmissing colors of each image pixel for generating distinct values ofprimary colors or complementary base hues for each image pixel, themethod comprising: calculating spatial variation gradients of primarycolors or complementary base hues for each image pixel and storingdirectional variation information of a primary color or complementarybase hue in look-up tables pertaining to each pixel; and interpolatingvalues of each image pixel based upon the directional variationinformation of the respective values of primary colors or complementarybase hues stored in the respective look-up tables of the pixel forgenerating multiple distinct values for each image pixel.
 2. The methodaccording to claim 1, wherein the spatial variation gradients havecomponents along two coordinate directions that are calculated usingSobel operators on sensed values of the image pixels, and the look-uptables are generated using for each image pixel a “weighted-mode”operator over a window centered on a selected image pixel, andcomprising storing amplitude values and quantized direction values ofthe spatial variation gradients for each pixel around a central pixel asa function of the calculated components; for each quantized orientation,calculating a sum of the amplitudes of the gradients oriented in adirection for each of the pixels contained in the window; establishingwhich of the sums is a largest sum; and calculating the amplitude valueof the gradient associated with the central pixel as a function of thelargest sum.
 3. The method according to claim 1, wherein theinterpolation of the respective values of primary colors or base hues iscarried out using an elliptical Gaussian filter whose orientationcoincides with the calculated direction of the relative spatial gradientfor the pixel being processed.
 4. The method according to claim 3,further comprising enhancing high spatial frequency components ofmissing pixels by choosing a selection window of pre-established sizecentered on an image pixel; calculating a difference (Δ_(Peak)) betweena present value of the central pixel and a value thereof calculated withthe elliptical Gaussian filter; enhancing the high frequency componentsof central pixels missing in the selection window by adding thedifference (Δ_(Peak)) to their interpolated value.
 5. A method fordetermining amplitude values and orientation values of a spatialgradient for a pixel belonging to an image acquired by a digital colorsensor generating grey level values for each image pixel as a functionof a filter applied to the sensor, the method comprising: a) choosing aselection window of a predefined size centered on the image pixel; b)calculating spatial variation values along two orthogonal directionsthrough Sobel operators applied to pixels selected by the window; c)calculating amplitude and orientation of the spatial gradient as afunction of the spatial variation components; d) quantizing thecalculated orientation, thus determining an orientation to be determinedof the gradient in the image pixel; e) repeating steps b) to d) for eachpixel contained in the selection window; f) for each quantizedorientation, calculating a sum of the amplitudes of the gradients withthe orientation for each pixel contained in the window; g) determiningwhich of the sums is the largest sum; h) calculating the amplitude valueof the gradient associated to the central pixel as a function of thelargest sum.
 6. A processor readable storage medium containing processorreadable code for programming a processor to perform a method of colorinterpolation of an image acquired by a digital color sensor generatinggrey level values for each image pixel as a function of a filter appliedto the sensor by interpolating values of missing colors of each imagepixel for generating distinct values of primary colors or complementarybase hues for each image pixel, the method comprising: calculatingspatial variation gradients of primary colors or complementary base huesfor each image pixel and storing directional variation information of aprimary color or complementary base hue in look-up tables pertaining toeach pixel; and interpolating values of each image pixel based upon thedirectional variation information of the respective values of primarycolors or complementary base hues stored in the respective look-uptables of the pixel for generating multiple distinct values for eachimage pixel.
 7. The processor readable storage medium according to claim6, wherein the spatial variation gradients have components along twocoordinate directions that are calculated using Sobel operators onsensed values of the image pixels, and the look-up tables are generatedusing for each image pixel a “weighted-mode” operator over a windowcentered on a selected image pixel, and comprising storing amplitudevalues and quantized direction values of the spatial variation gradientsfor each pixel around a central pixel as a function of the calculatedcomponents; for each quantized orientation, calculating a sum of theamplitudes of the gradients oriented in a direction for each of thepixels contained in the window; establishing which of the sums is alargest sum; and calculating the amplitude value of the gradientassociated with the central pixel as a function of the largest sum. 8.The processor readable storage medium according to claim 6 wherein theinterpolation of the respective values of primary colors or base hues iscarried out using an elliptical Gaussian filter whose orientationcoincides with the calculated direction of the relative spatial gradientfor the pixel being processed.
 9. The processor readable storage mediumaccording to claim 6, further comprising enhancing high spatialfrequency components of missing pixels by choosing a selection window ofpre-established size centered on an image pixel; calculating adifference (Δ_(Peak)) between a present value of the central pixel and avalue thereof calculated with the elliptical Gaussian filter; enhancingthe high frequency components of central pixels missing in the selectionwindow by adding the difference (Δ_(Peak)) to their interpolated value.10. An electronic device for color interpolation of an image acquired bya digital color sensor generating grey level values for each image pixelas a function of a filter applied to the sensor by interpolating valuesof missing colors of each image pixel for generating distinct values ofprimary colors or complementary base hues for each image pixel, theelectronic device comprising: a processing subsystem cooperating withthe sensor to calculate spatial variation gradients of primary colors orcomplementary base hues for each image pixel and storing directionalvariation information of a primary color or complementary base hue inlook-up tables pertaining to each pixel; and interpolate values of eachimage pixel based upon the directional variation information of therespective values of primary colors or complementary base hues stored inthe respective look-up tables of the pixel for generating multipledistinct values for each image pixel.
 11. The electronic deviceaccording to claim 10, wherein said processing subsystem calculatescomponents along two coordinate directions of the spatial variationgradients using Sobel operators on sensed values of the image pixels,and said processing subsystem generates the look-up tables using a“weighted-mode” operator over a window centered on a selected imagepixel for each image pixel by storing amplitude values and quantizeddirection values of the spatial variation gradients for each pixelaround a central pixel as a function of the calculated components, foreach quantized orientation, calculating a sum of the amplitudes of thegradients oriented in a direction for each of the pixels contained inthe window, establishing which of the sums is a largest sum, andcalculating the amplitude value of the gradient associated with thecentral pixel as a function of the largest sum.
 12. The electronicdevice according to claim 10, wherein said processing subsysteminterpolates the respective values of primary colors or base hues usingan elliptical Gaussian filter whose orientation coincides with thecalculated direction of the relative spatial gradient for the pixelbeing processed.
 13. The electronic device according to claim 12,wherein said processing subsystem enhances high spatial frequencycomponents of missing pixels by choosing a selection window ofpre-established size centered on an image pixel, calculating adifference (Δ_(Peak)) between a present value of the central pixel and avalue thereof calculated with the elliptical Gaussian filter, andenhancing the high frequency components of the central pixels missing inthe selection window by adding the difference (Δ_(Peak)) to theirinterpolated value.